Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a highly spectral efficient technology used to transmit high-speed data through radio channels with fast fading both in frequency and in time.
In wireless communication systems that employ OFDM, a transmitter transmits data to a receiver using many sub-carriers in parallel. The frequencies of the sub-carriers are orthogonal. Transmitting the data in parallel allows the symbols containing the data to be of longer duration, which mitigates the effects of multi-path fading in particular dispersion. The orthogonality of the frequencies allows the sub-carriers to be tightly spaced, while minimising inter-carrier interference. At the transmitter, the data is encoded, interleaved, and modulated to form data symbols. Overhead information is added, including pilot symbols, and the symbols (data plus overhead) are organised into OFDM symbols. Each OFDM symbol typically uses 2n frequencies.
Each symbol is allocated to represent a component of a different orthogonal frequency. An inverse Fast Fourier Transform (IFFT) is applied to the OFDM symbol (hence the preference of 2n frequencies) to generate time samples of a signal. Cyclic extensions are added to the signal, and the signal is passed through a digital-to-analog converter. Finally, the transmitter transmits the signal to the receiver along a channel.
When the receiver receives the signal, the inverse operations are performed. The received signal is passed through an analog-to-digital converter, and timing information is then determined. The cyclic extensions are removed from the signal. The receiver performs a FFT on the received signal to recover the frequency components of the signal, that is, the data symbols. Error correction may be applied to the data symbols to compensate for variations in phase and amplitude caused during propagation of the signal along the channel. The data symbols are then demodulated, de-interleaved, and decoded, to yield the transmitted data.
In systems employing differential detection, the receiver compares the phase and/or amplitude of each received symbol with an adjacent symbol. The adjacent symbol may be adjacent in the time direction or in the frequency direction. The receiver recovers the transmitted data by measuring the change in phase and/or amplitude between a symbol and the adjacent symbol. If differential detection is used, channel compensation need not be applied to compensate for variations in phase and amplitude caused during propagation of the signal. However, in systems employing coherent detection the receiver must estimate the actual phase and amplitude of the channel response, and channel compensation must be applied.
The variations in phase and amplitude resulting from propagation along the channel are referred to as the channel response. The channel response is usually frequency and time dependent. If the receiver can determine the channel response, the received signal can be corrected to compensate for the channel degradation. The determination of the channel response is called channel estimation. The inclusion of pilot symbols in each OFDM symbol allows the receiver to carry out channel estimation. The pilot symbols are transmitted with a value known to the receiver. When the receiver receives the OFDM symbol, the receiver compares the received value of the pilot symbols with the known transmitted value of the pilot symbols to estimate the channel response.
The pilot symbols are overhead, and should be as few in number as possible in order to maximise the transmission rate of data symbols. Since the channel response can vary with time and with frequency, the pilot symbols are scattered amongst the data symbols to provide as complete a range as possible of channel response over time and frequency. The set of frequencies and times at which pilot symbols are inserted is referred to as a pilot pattern. The optimal temporal spacing between the pilot symbols is usually dictated by the maximum anticipated Doppler frequency, and the optimal frequency spacing between the pilot symbols is usually dictated by the anticipated delay spread of multi-path fading.
The existing pilot-assisted OFDM channel estimation approaches are designed for conventional one transmitter systems. With a scattered pilot arrangement, there are three classes of algorithms:                1-D frequency interpolation or time interpolation        Transformed frequency 1-D interpolation        Independent time and frequency 1-D interpolation        
The first class of algorithms is based on the pilot OFDM symbol (all of the sub-carriers are used as the pilots) or comb-type of pilots. This approach, shown in the flow chart of FIG. 1A, is simple but only suitable for channels with high frequency selectivity or channels with high time fading. The method involves pilot extraction in the frequency domain (step 1A-1) followed by interpolation in time (step 1A-2), or interpolation in frequency (step 1A-3).
The second method shown in the flow chart of FIG. 1B is aimed for channels with slow Doppler fading and fast frequency fading. It improves the first method by using a FFT to reconstruct the channel response back to time domain for noise reduction processing at the expense of FFT/IFFT computing for the channel estimation separately. The method begins with pilot extraction in the frequency domain (step 1B-1), which may be followed by interpolation in frequency (step 1B-2). Then an inverse fast Fourier transform (step 1B-3), smoothing/de-noise processing (step 1B-4), and finally a fast Fourier transform (1B-5) steps are executed.
The third method shown in the flow chart of FIG. 1C can be used to estimate channel for mobile applications, where both fast time fading and frequency fading exist. However, it needs a relatively high density of pilots and a completed interpolator. This method involves pilot extraction in the frequency domain (step 1C-1) this is followed by interpolation in time (step 1C-2) and interpolation in frequency (step 1C-3).
In the propagation environment with both high time dispersion and rapid temporal fading, the channel estimation performance can be improved by the increase of pilot symbol density at the price of the reduction of the spectral efficiency of the data transmission. To interpolate and reconstruct the channel response function from the limited pilots to achieve reliable channel estimation with the minimum overhead is a challenging task.
There are a variety of existing standard pilot patterns. In environments in which the channel varies only slowly with time and frequency, the pilot symbols may be inserted cyclically, being inserted at an adjacent frequency after each time interval. In environments in which the channel is highly frequency dependent, the pilot symbols may be inserted periodically at all frequencies simultaneously. However, such a pilot pattern is only suitable for channels that vary very slowly with time. In environments in which the channel is highly time dependent, the pilot symbols may be inserted continuously at only specific frequencies in a comb arrangement to provide a constant measurement of the channel response. However, such a pilot pattern is only suitable for channels that vary slowly with frequency. In environments in which the channel is both highly frequency and highly time dependent (for example, mobile systems with much multi-path fading), the pilot symbols may be inserted periodically in time and in frequency so that the pilot symbols form a rectangular lattice when the symbols are depicted in a time-frequency diagram.
In OFDM communication systems employing coherent modulation and demodulation, the receiver must estimate the channel response at the frequencies of all sub-carriers and at all times. This requires more processing than in systems that employ differential modulation and demodulation. However a significant improvement in throughput at any given signal-to-noise ratio can be achieved using coherent modulation and demodulation (for a given error probability). The receiver determines the channel response at the times and frequencies at which pilot symbols are inserted into the OFDM symbol, and performs interpolations to estimate the channel response at the times and frequencies at which the data symbols are located within the OFDM symbol. Placing pilot symbols more closely together (in frequency if a comb pattern is used, in time if a periodic pattern is used, or in both frequency and in time if a rectangular lattice pattern is used) within a pilot pattern results in a more accurate interpolation. However, because pilot symbols constitute an overhead, a tighter pilot pattern comes at the expense of the useful transmitted data rate.
Existing pilot patterns and interpolation techniques are usually sufficient if the channel varies slowly with time (for example for nomadic applications). However, if the channel varies quickly with time (for example, for mobile applications), the time interval between pilot symbols must be reduced in order to allow an accurate estimation of the channel response through interpolation. This increases the overhead in the signal.
The problem of minimising the number of pilot symbols while maximising the accuracy of the interpolation is also particularly cumbersome in Multiple-Input Multiple-Output (MIMO) OFDM systems. In MIMO OFDM systems, the transmitter transmits data through more than one transmitting antenna and the receiver receives data through more than one receiving antenna. The binary data is usually divided between the transmitting antennas, although the same data may be transmitted through each transmitting antenna if spatial diversity is desired. Each receiving antenna receives data from all the transmitting antennas, so if there are M transmitting and N receiving antennas, then the signal will propagate over M×N channels, each of which has its own channel response. Each transmitting antenna inserts pilot symbols into the same sub-carrier location of the OFDM symbol which it is transmitting. In order to minimise interference at the receiver between the pilot symbols of each transmitting antenna, each transmitting antenna typically blinks its pilot pattern on and off. This increases the temporal separation of the pilot symbols for each transmitter, reducing the accuracy of the interpolation used to estimate the channel response.
In MIMO-OFDM systems a simple and fast channel estimation method is particularly crucial because of the limitation of the computational power for estimating M×N channels, while in SISO-OFDM systems (Single Input Single Output-OFDM) only one channel needs to be estimated.
In our co-pending U.S. patent application Ser. No. 10/038,883, there are described channel estimation methods which are based on the partial interpolation of a scattered pilot by using true 2D interpolation; and additionally, simple 1D interpolation used to reconstruct the entire channels. This method has a reduced scattered pilot overhead and is at least an order of magnitude less computationally complex than some existing methods.
Although the methods proposed in the above application provide better channel estimation than some existing methods, it is observed that the scattered pilot and receiver channel filter parameters are pre-set and optimised for some “average” channel condition. In practice it is very likely that the receiver will be in an environment that does not match this average situation in which case, the accuracy of the channel estimation is limited by these “average” settings, which may adversely affect downlink throughput to the receiver.